The Netslab-5G Open RAN Intrusion Detection Dataset (Netslab-5GORAN-IDD) is a real-world dataset collected using a physical 5G Open RAN testbed at University College Dublin. It includes both higher-layer network traffic data and lower-layer radio and device-level metrics, making it one of the few comprehensive datasets available for research in Open RAN security. This dataset was […]
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The upcoming Sixth Generation (6G) networks aim for fully automated, intelligent network functionalities and services. Therefore, Machine Learning (ML) is essential for these networks. Given stringent privacy regulations, future network architectures should use privacy-preserved ML for their applications and services. Federated Learning (FL) is expected to play an important role as a popular approach for […]
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Large Language Models (LLMs) have revolutionized natural language processing, but deploying them in resource-constrained environments and privacy-sensitive domains remains challenging. This paper introduces the Distributed Large Language Model (DisLLM), a novel distributed learning framework that addresses privacy preservation and computational efficiency issues in LLM fine-tuning and inference. DisLLM leverages the Splitfed Learning (SFL) approach, combining […]
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The upcoming Beyond 5G (B5G) and 6G networks are expected to provide enhanced capabilities such asultra-high data rates, dense connectivity, and high scalability. It opens many possibilities for a new generation of services driven by Artificial Intelligence (AI) and billions of interconnected smart devices. However, with this expected massive upgrade, the privacy of people, organizations, […]
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With the rapid progression of communication and localisation of big data over billions of devices, distributed Machine Learning (ML) techniques are emerging to cater for the development of Artificial Intelligence (AI)-based services in a distributed manner. Federated Learning (FL) is such an innovative approach to achieve a privacy-preserved AI that facilitates ML model sharing and […]
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An emergence of attention and regulations on consumer privacy can be observed over the recent years with the ubiquitous availability of IoT systems handling personal data. Federated Learning (FL) arises as a privacy-preserved Machine Learning (ML) technique where data can be kept private within these devices without transmitting to third parties. Yet, many privacy attacks […]
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A wide adoption of Artificial Intelligence (AI) can be observed in recent years over networking to provide zero-touch, full autonomy of services towards the next generation Beyond 5G (B5G)/6G. However, AI-driven attacks on these services are a major concern in reaching the full potential of this future vision. Identifying how resilient the AI models are […]
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Federated learning (FL) is an intriguing approach to privacy-preserving collaborative learning. Decentralised FL is achieving increased favour for investigation due to the mitigation of vulnerability for a single point of failure and more controllability for end users over their models. However, many existing decentralised FL systems face limitations, such as privacy concerns, latency in aggregation, […]
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Federated Learning (FL) is an emerging privacy-preserved distributed Machine Learning (ML) technique where multiple clients can contribute to training an ML model without sharing private data. Even though FL offers a certain level of privacy by design, recent works show that FL is vulnerable to numerous privacy attacks. One of the key features of FL […]
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Metaverse is a key emerging digital transformation concept for the next generation of cyberspace. It is expected to create a self-sustaining virtual ecosystem of fully immersive, real-time experiences with numerous opportunities for general users and industries to interact with the world. With the introduction of 6G networks and enabling technologies, the metaverse will achieve its […]
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